Improving multiple stressor-response models through the inclusion of nonlinearity and interactions among stressor gradients.

Stressor-response models are used to detect and predict changes within ecosystems in response to anthropogenic and naturally occurring stressors. While nonlinear stressor-response relationships and interactions between stressors are common in nature, predictive models often do not account for them due to perceived difficulties in the interpretation of results. We used Irish river monitoring data from 177 river sites to investigate if multiple stressor-response models can be improved by accounting for nonlinearity, interactions in stressor-response relationships and environmental context dependencies. Out of the six models of distinct biological responses, five models benefited from the inclusion of nonlinearity while all six benefited from the inclusion of interactions. The addition of nonlinearity means that we can better see the exponential increase in Trophic Diatom Index (TDI3) as phosphorus increases, inferring ecological conditions deteriorating at a faster rate with increasing phosphorus. Furthermore, our results show that the relationship between stressor and response has the potential to be dependent on other variables, as seen in the interaction of elevation with both siltation and nutrients in relation to Ephemeroptera, Plecoptera and Trichoptera (EPT) richness. Both relationships weakened at higher elevations, perhaps demonstrating that there is a decreased capacity for resilience to stressors at lower elevations due to greater cumulative effects. Understanding interactions such as this is vital to managing ecosystems. Our findings provide empirical support for the need to further develop and employ more complex modelling techniques in environmental assessment and management.

The dynamics of picocyanobacteria from a hypereutrophic shallow lake is affected by light-climate and small-bodied zooplankton: a 10-year cytometric time-series analysis.

In aquatic systems, an interplay between bottom-up and top-down processes determines the dynamic of picocyanobacteria (Pcy) abundance and community structure. Here, we analyzed a 10-year time series (sampled fortnightly) from a hypereutrophic turbid shallow lake located within the Pampa Region of South America, generating the first long-term record of freshwater Pcy from the Southern Hemisphere. We used a cytometric approach to study Pcy community, and focused on its relations with nutrient and light conditions (bottom-up) and potential grazers (top-down). A novel Pcy abundance seasonality with winter maximums was observed for years with relatively stable hydrological levels, related with decreased abundance of seasonal rotifers during colder seasons. Pcy showed lower abundance and higher cytometric alpha diversity during summer, probably due to a strong predation exerted by rotifers. In turn, a direct effect of the non-seasonal small cladocerans Bosmina spp. decreased Pcy abundance and induced a shift from single-cell Pcy into aggregated forms. This structuring effect of Bosmina spp. was further confirmed by Pcy cytometric (dis)similarity analyses from the time series and in situ experimental data. Remarkably, Pcy showed acclimatization to underwater light variations, resembling the relevance of light in this turbid system.

Driving safety assessment for ride-hailing drivers.

Ride-hailing services, which have become increasingly prevalent in the last decade, provide an efficient travel mode by matching drivers and travelers via smartphone apps. Ride-hailing services enable millions of non-traditional taxi drivers to provide travel services, but may also raise safety concerns due to heterogeneity in the driver population. This study evaluated crash risk factors for ride-hailing drivers, including driving history and ride-hailing operational characteristics, using a sample of 189,815 drivers. We utilized the Poisson generalized additive model to accommodate for the potential nonlinear relationship between crash rate and risk factors. Results showed that crash history, the percentage of long-shift bookings, driving distance, operations during peak hours, years of being a ride-hailing driver, and passenger rating were significantly associated with crash risk. Several factors showed nonlinear relationships with crash risk. We adopted the SHapley Additive exPlanation (SHAP) method to assess and visualize the impact of each risk factor. The results indicated that passenger average rating, total driving distance, and crash history were the leading contributing factors. The findings of this study provide critical information for the development of safety countermeasures, driver education programs, as well as safety regulations for the ride-hailing industry.

Residential Altitude Associates With Endurance but Not Muscle Power in Young Swiss Men.

INTRODUCTION: Physical fitness benefits health. However, there is a research gap on how physical fitness, particularly aerobic endurance capacity and muscle power, is influenced by residential altitude, blood parameters, weight, and other cofactors in a population living at low to moderate altitudes (300-2100 masl). MATERIALS AND METHODS: We explored how endurance and muscle power performance changes with residential altitude, Body Mass Index (BMI), hemoglobin and creatinine levels among 108,677 Swiss men aged 18-22 years (covering >90% of Swiss birth cohorts) conscripted to the Swiss Armed Forces between 2007 and 2012. The test battery included a blood test of about 65%, a physical evaluation of about 85%, and the BMI of all conscripts. RESULTS: Residential altitude was significantly associated with endurance (p < 0.001) but not with muscle power performance (p = 0.858) after adjusting for all available cofactors. Higher BMI showed the greatest negative association with both endurance and muscle power performance. For muscle power performance, the association with creatinine levels was significant. Elevated C-reactive protein (CRP) and hemoglobin levels were stronger contributors in explaining endurance than muscle power performance. CONCLUSION: We found a significant association between low to moderate residential altitude and aerobic endurance capacity even after adjustment for hemoglobin, creatinine, BMI and sociodemographic factors. Non-assessed factors such as vitamin D levels, air pollution, and lifestyle aspects may explain the presented remaining association partially and could also be associated with residential altitude. Monitoring the health and fitness of young people and their determinants is important and of practical concern for disease prevention and public health implications.

Plant functional traits differ in adaptability and are predicted to be differentially affected by climate change.

Climate change is testing the resilience of forests worldwide pushing physiological tolerance to climatic extremes. Plant functional traits have been shown to be adapted to climate and have evolved patterns of trait correlations (similar patterns of distribution) and coordinations (mechanistic trade-off). We predicted that traits would differentiate between populations associated with climatic gradients, suggestive of adaptive variation, and correlated traits would adapt to future climate scenarios in similar ways.We measured genetically determined trait variation and described patterns of correlation for seven traits: photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), leaf size (LS), specific leaf area (SLA), δ13C (integrated water-use efficiency, WUE), nitrogen concentration (NCONC), and wood density (WD). All measures were conducted in an experimental plantation on 960 trees sourced from 12 populations of a key forest canopy species in southwestern Australia.Significant differences were found between populations for all traits. Narrow-sense heritability was significant for five traits (0.15-0.21), indicating that natural selection can drive differentiation; however, SLA (0.08) and PRI (0.11) were not significantly heritable. Generalized additive models predicted trait values across the landscape for current and future climatic conditions (>90% variance). The percent change differed markedly among traits between current and future predictions (differing as little as 1.5% (δ13C) or as much as 30% (PRI)). Some trait correlations were predicted to break down in the future (SLA:NCONC, δ13C:PRI, and NCONC:WD).Synthesis: Our results suggest that traits have contrasting genotypic patterns and will be subjected to different climate selection pressures, which may lower the working optimum for functional traits. Further, traits are independently associated with different climate factors, indicating that some trait correlations may be disrupted in the future. Genetic constraints and trait correlations may limit the ability for functional traits to adapt to climate change.

Satellite and historical data, and statistical modeling to predict potential fishing zones for dolphinfish, Coryphaena hippurus (Perciformes: Coryphaenidae) in Colombian Pacific / Datos satelitales e históricos, y modelado estadístico, para predecir zonas de pesca potencial del pez dorado, Coryphaena hippurus (Perciformes: Coryphaenidae) en el Pacífico colombiano

Introduction: The prediction of potential fishing areas is considered one of the most immediate and practical approaches in fisheries and is an essential technique for decision-making in managing fishery resources. It helps fishermen reduce their fuel costs and the uncertainty of their fish catches; this technique allows to contribute to national and international food security. In this study, we build different combinations of predictive statistical models such as Generalized Linear Models and Generalized Additive Models. Objective: To predict the spatial distribution of PFZs of the dolphinfish (Coryphaena hippurus L.) in the Colombian Pacific Ocean. Methods: We built different combinations of Generalized Linear Models and Generalized Additive Models to predict the Catch Per Unit Effort of C. hippurus captured from 2002 to 2015 as a function of sea surface temperature, chlorophyll-a concentration, sea level anomaly, and bathymetry. Results: A Generalized Additive Model with Gaussian error distribution obtained the best performance for predicting PFZs for C. hipurus. Model validation was performed by calculating the Root Mean Square Error through a cross-validation approach. The R2 of this model was 50 %, which was considered suitable for the type of data used. January and March were the months with the highest Catch per Unit Effort values, while November and December showed the lower values. Conclusion: The predicted PFZs of C. hippurus with Generalized Additive Models satisfactorily with the results of previous research, suggesting that our model can be explored as a tool for the assessment, decision making, and sustainable use of this species in the Colombian Pacific Ocean.

Introducción: La predicción de zonas potenciales de pesca se considera uno de los enfoques más inmediatos y efectivos en las pesquerías, es una técnica importante para la toma de decisiones en el manejo de los recursos pesqueros. Ayuda a los pescadores a reducir su costo de combustible y también a disminuir la incertidumbre de sus capturas, esta técnica permite contribuir a la seguridad alimentaria nacional e internacional. En este estudio, se construyeron diferentes combinaciones de modelos estadísticos predictivos como modelos lineales generalizados y modelos aditivos generalizados. Objetivo: predecir la distribución espacial de las zonas potenciales de pesca del pez dorado (Coryphaena hippurus L.) en el Pacífico colombiano. Métodos: La variable de respuesta se expresó en escala de captura por unidad de esfuerzo, es decir, el número de individuos de C. hippurus capturados por un número total de anzuelos disponibles entre 2002 y 2015. Temperatura de la superficie del mar, concentración de clorofila, anomalía del nivel del mar y batimetría, se utilizaron como variables explicativas para los meses de estacionalidad de C. hippurus (noviembre - marzo). Resultados: El modelo con mejor rendimiento para la predicción de zonas potenciales de pesca fue un modelo aditivo generalizado con distribución de error gaussiana y función de enlace de registro, que se seleccionó en función del criterio de información de Akaike, el R2 y la desviación explicada. La validación del modelo se realizó calculando el error cuadrático medio a través de un enfoque de validación cruzada. El ajuste de este modelo fue del 50 %, lo que puede considerarse adecuado para el tipo de datos utilizados. Enero y marzo fueron los meses con mayor captura por unidad de esfuerzo y noviembre-diciembre los meses con menor. Conclusión: Las zonas potenciales de pesca previstas coincidieron satisfactoriamente con investigaciones anteriores, lo que sugiere que nuestro modelo es una herramienta poderosa para la evaluación, toma de decisiones y uso sostenible de los recursos pesqueros de C. hippurus en el Pacífico colombiano.